Job Title

Job Summary:We are building the next-generation smart facilities platform — one that goes far beyond traditional BMS. At its core is a live digital twin of every building we deploy in a real-time, physics-informed virtual replica of its mechanical, electrical, and environmental systems.This role is for a hands-on Product Lead who can both architect and lead. You will own the full stack from edge device to cloud analytics, define product direction, and mentor a growing engineering team. If you have deep expertise at the intersection of IoT, controls, and scalable software — and are excited by the idea of buildings that think — this role is for you.Responsibilities:End-to-end platform architecture:Edge → Gateway → Cloud / On-Prem → Analytics → Control feedback loopDigital Twin Development:Build and maintain real-time building digital twins using physics-based and data-driven modelsModel HVAC topology, asset hierarchies, and operational states in a live twin environmentIntegrate BIM data, sensor streams, and historical operations data into the twinProtocol & Systems Integration:BACnet, Modbus, OPC-UA, MQTT — connecting real building systems to the platformReal-time data ingestion pipelines and time-series analytics enginesControl strategy development: HVAC optimization, demand response, fault detectionProduct thinking: translate facility-level problems into software abstractions and roadmapMentor and lead a team of 5–6 engineers across backend, integration, and analyticsEducation and Experience:5+ years in IoT / Industrial IoT / Building Automation platformsDeep understanding of HVAC systems: chillers, AHUs, VAVs, VRF, cooling towersHands-on experience with BACnet, Modbus, OPC-UA, MQTTDigital Twin experience:Building or industrial digital twin platforms (e.g., Azure Digital Twins, AVEVA, Siemens Xcelerator, custom)Working knowledge of twin modelling concepts: ontologies, asset graphs, state synchronizationStrong backend engineering in Python — the primary language for data pipelines, analytics, and AI integrationAI & Machine Learning Integration:Experience embedding AI/ML models into production systems: predictive maintenance, anomaly detection, load forecastingFamiliarity with LLM APIs (OpenAI, Anthropic) or AI orchestration frameworks (LangChain, LlamaIndex) for building intelligent featuresExperience with time-series databases: InfluxDB, TimescaleDB, or similarProven ability to lead engineering teams and drive product decisions